Adrienne Kline reflects on a multidisciplinary career that bridges medicine, machine learning, and engineering — In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Adrienne Kline. Adrienne Kline is a postdoctoral fellow in the Department of Preventative Medicine at…

It’s never a bad idea to reevaluate the way we approach our work. This is true whether you’re a student working on a portfolio project, a seasoned ML engineer managing an end-to-end pipeline, or an executive responsible for the success of an entire data team. It could be a superfluous…

Time series—sequences of events or values that we can analyze to detect meaningful patterns—are a key element in data scientists’ toolkit. …

It took just over a decade (give or take) for data science and machine learning to grow from ancillary functions within businesses into major lucrative industries in their own right. …

Julia Turc discusses her career path in natural language processing and the future of multimodal machine learning — In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Julia Turc. Julia is a software engineer and researcher in Natural Language Processing, with particular…

Your expertise + our editorial review process = success! — At TDS, we invest a lot of time and care into sharing well-written and clearly explained articles with our audience. Our community of volunteer Editorial Associates plays a major role in maintaining the quality of our publication: our editors loop in EAs whenever their subject-matter expertise is particularly helpful. Sometimes…

If you look closely enough at any strict category, sooner or later it starts to fall apart. The common division of data scientists’ skills into “hard” and “soft” is no exception. Code documentation: a soft skill or a hard one? What about creating a sharp-looking chart, or setting up a…

From interactive maps to advanced georeferencing — We’re not too shy to admit it: we have a soft spot for geospatial data analysis. Why? Let us count the ways! It’s a subfield of data science that brings together multiple disciplines and practices, from geography and programming to cartography and geometry. Geospatial projects connect the digital and physical…

There are many paths into a data science career, and not all of them involve a strong programming background. When we hear data practitioners second-guess themselves and their abilities, a recurring theme is code: “Am I even a real data scientist if I don’t know how to [insert your own…